Estimating a Survival Distribution with Current Status Data and High-dimensional Covariates

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 1900-01-01 DOI:10.2202/1557-4679.1014

A. van der Vaart, M. J. van der Laan

引用次数: 47

Abstract

We consider the inverse problem of estimating a survival distribution when the survival times are only observed to be in one of the intervals of a random bisection of the time axis. We are particularly interested in the case that high-dimensional and/or time-dependent covariates are available, and/or the survival events and censoring times are only conditionally independent given the covariate process. The method of estimation consists of regularizing the survival distribution by taking the primitive function or smoothing, estimating the regularized parameter by using estimating equations, and finally recovering an estimator for the parameter of interest.

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用当前状态数据和高维协变量估计生存分布

我们考虑当生存时间仅在时间轴随机平分的一个区间内观察到时估计生存分布的反问题。我们对高维和/或时间相关协变量可用的情况特别感兴趣，并且/或生存事件和审查时间仅在给定协变量过程的条件下独立。估计方法包括采用原始函数或平滑对生存分布进行正则化，利用估计方程估计正则化后的参数，最后恢复目标参数的估计量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.